User Characteristics That Influence e-Health Tool Use

Public health interventions typically rely on broad demographic
categories to identify who is affected by an issue, risk factor,
or disease. Those most affected become the targets for an intervention.
These demographic categories—including race, ethnicity,
gender, age, income and education levels, and disability status,
among others—are the basis for much of the current debate
on the nature and extent of health disparities (HHS
2000, 2005a).

One of the original purposes of the present study—a
purpose that could not be wholly fulfilled because of a lack
of existing research and publicly available data—was
to identify and analyze factors in addition to demographics
that affect the adoption of e-health tools by those population
segments most affected by health disparities. As noted throughout
the report, studies suggest that populations that experience
health disparities are also likely to experience disparities
in technology access and use. Beyond these broad observations,
however, little information addresses factors related to users’
motivation, engagement, and understanding of e-health tools
and their relevance to strategies to promote greater use. The
IOM Committee on Communication for Behavior Change in the 21st
Century found that “data that provide a much deeper and
more sophisticated understanding of how specific beliefs and
behaviors and health status covary across the U.S. population
and of how health behavior is shaped by sociocultural processes
are not available. . . .” (IOM,
2002, p. 15).

Demographic characteristics or functional skills, such as
low literacy, novice computer skills, and limited English proficiency,
are the main factors that have been used to characterize user
groups to date. Gender, education, income, and age are strong
determinants of interest and behavior in health information-seeking
across media, according to a review of prevention communication
and media use (Lieberman, Benet, Lloyd-Kolkin,
et al., 2004). Regardless of ethnicity, well-educated,
affluent women under age 65 are the most active health information
consumers.

Studies suggest that race and ethnicity have some association
with communication processes, perhaps because of the ways that
race can act as a marker or proxy for cultural factors. The
literature review conducted for this study (see Chapter
3) found that few studies explicitly assessed the significance
of race, ethnicity, or culture on participants’ interaction
with and response to technologies. A few studies did recruit
participants on the basis of racial and ethnic characteristics,
but they did not explore the significance of cultural influences.

Race and ethnicity are highly significant variables for health
status, if only because of the impact of discrimination on
health disparities. However, there is often more variation
within traditional demographic categories than between them.
Moreover, the IOM Committee on Communication for Behavior Change
in the 21st Century cautions that the use of overly broad or
rigid demographic characteristics can actually exacerbate inequities
by reinforcing inaccurate assumptions and stereotypes. This
Committee calls for a focus on “more meaningful ways
of describing heterogeneity,” focused on cultural processes,
life experience, sociocultural environment, economic contexts,
community resources, and beliefs (IOM,
2002).

From a communication perspective, people attribute meaning
and make sense of the messages, interactions, situations, and
media around them; and they interact with and shape both the
tools and the environments in which they live. Interactive
media, including e-health tools, make these processes more
obvious because they provide new opportunities to act as engaged
users instead of passive receivers of information, “link(ing),
think(ing) and interact(ing)” with information and other
users (Cole, 2004). Individuals
become involved in shaping an environment of highly personalized
and private engagement with the Internet, Web sites, and interactive
components.

Some researchers conceptualize the Internet as a “hybrid”
medium with features of mass and interpersonal communication
(Cassell, Jackson, and
Cheuvront, 1998). Some of the many communication factors
relevant to the analysis of e-health tools are patterns of
media or technology use, values, beliefs, intentions, expectations,
preferences, perspectives, capacities, and access to information
and technology (Neuhauser
and Kreps, 2003). The characteristics of technology are
important in terms of its fit with, value for, and usability
by different user groups (Badre,
2002; Nielsen, 1999;
Norman, 2002).

The lack of research on psychosocial variables other than
health information-seeking as well as the lack of multivariate
analyses of demographic and communication factors are major
gaps in the literature (Lieberman
et al., 2004). A few studies have examined the motivations
or level of interest of potential or actual users of e-health
tools—typically health information Web sites, online
communities, or provider-patient e-mails. It is easier to know
who, in demographic terms, is or is not using computers and
the Internet than it is to know how individuals think about
what they do online and how the interaction reinforces or changes
their attitudes, beliefs, values, and preferences.

Despite the paucity of research, however, some things are
known about factors that influence health communication processes
and audiences’ interactions with media. The most influential
characteristics that have some evidence of their relevance
are discussed briefly below.

Language Spoken

The relevance of language spoken to the use of e-health tools
cannot be overstated. If individuals or groups use one language
and the tool is based on a different language, users are very
unlikely to make sense of the tool and the content. English-language
materials dominate the Internet, which limits the utility of
the content for those who read little or no English (The
Children’s Partnership, 2000).

Approximately 19 percent of the population speaks a language
other than English, according to 2004 Census Bureau data (U.S.
Census Bureau, 2004). The majority of persons in this category
are Spanish speakers (62 percent); Chinese is a distant second.
Data from the Census and the U.S. Department of Education suggest
that the majority of persons who speak a language other than
English at home consider themselves able to function “very
well” in English (Greenberg
et al., 2001; U.S.
Census Bureau, 2000). Overall, the Census Bureau reports
that 92 percent of the population over the age of 5 years report
that they do not have difficulty functioning in English (U.S.
Census Bureau, 2000). Census data indicate that approximately
4 percent of the population is “linguistically isolated”
(U.S. Census Bureau,
2000). Despite this picture of English-language functioning,
these data do not speak to issues of language preferences of
different groups, the significance of language as an element
of culture, or the role of language in perceptions of health
and illness.

“Linguistic appropriateness” may seem straightforward,
but it is not. Fulfilling the proviso that communication should
be in the primary language of the target audience is not simple
for large and diverse population groups, given the number of
versions of a given language. For example, Spanish speakers
present an interesting example of the complexities of linguistic
appropriateness. This population segment is both culturally
and linguistically diverse, coming primarily from multiple
countries in Latin America and the Caribbean and with distinct
cultural origins related primarily to Africa, indigenous America,
and Europe. Despite the cultural relevance of slang, dialect,
and vocabulary, there is often an imperative to identify a
“common” Spanish that will function cross-culturally
(Schroeder, Trowbridge, and Price,
2002). One of the few general studies of factors relevant
for Hispanic groups’ use of the Internet found that Hispanics
encounter many barriers when trying to locate Spanish-language
health information online (Schroeder et al., 2002).

At the same time, market research reports on Hispanics’
Internet use indicate that they are going online faster than
any other segment and are finding content of interest in the
categories of communication (e.g., instant messages), entertainment
(particularly music), and product information (Hispanic
Market Weekly, 2006). When they perceive the relevance
of the content, Hispanics are willing to go online to “compare
prices, see features, learn about benefits, and then decide
on a brand or purchase,” according to the publisher of
AOL Latino (cited in Hispanic
Market Weekly, 2006).

Small-scale studies of the health information needs and preferences
of Asian Americans, Native Hawaiians, Pacific Islanders, and
Native Americans suggest that lack of content in the first
languages of ethnic groups and inexperience with Internet resources
are major barriers to greater use (Hsu,
2003a, 2003b). However,
these factors have yet to be analyzed in terms of their contribution
to overall lower rates of Internet usage and demand for e-health
tools. For example, in a national survey of unpaid caregivers,
only 5 percent reported that “finding non-English educational
materials” was an unmet need (National
Alliance for Caregiving and AARP, 2004).

In the scan of e-health tools conducted for this report (see
Appendix 1), language and literacy
emerged as two critical considerations in the design of successful
tools. Even if developers did not report using any other methods
to account for audience variations, they did mention creating
understandable materials as design and content priorities.
Designing for a stated reading grade level seemed to be the
most popular strategy to make content more understandable.
Providing content in Spanish was the most popular alternative
to English.

Both these strategies have their own problems and raise a
number of issues concerning the utility and comprehensibility
of content. Even when content developers attempt translation,
the quality of translations and the readability of materials
can present problems. For example, translations can be of poor
quality and reproduce problems, such as jargon and unfamiliar
terms, that were features of the original text. Texts that
meet a stated reading grade level can still make it difficult
for users to understand the core meaning. Applying a health
literacy approach that engages intended users in the development
of the content from the beginning and focuses on assessing
usability and understanding seems the most promising mechanism
to address issues of language and literacy.

Socioeconomic Position

IOM proposes that the most important forms of diversity to
pay attention to in health communication are those associated
with “substantial disparities in health status and outcomes”
that also represent differences in “health behavior and
its antecedents” (IOM,
2002, p. 7). Individually and collectively, the components of
socioeconomic position—including income, employment status,
wealth, education, housing, and neighborhood environment—influence
health, health behavior, and factors involved in health communication.
IOM’s
Promoting Health report discusses the relationships
among these factors (2000).
Communication theory from the 1970s proposed the existence
of a “knowledge gap,” which represents the divide
between higher socioeconomic persons who pay closer attention
to and have greater access to information than lower socioeconomic
persons (Tichenor, Donohue, and Olien,
1970). In the e-health arena, socioeconomic factors are
major determinants of the elements of meaningful access, as
discussed above.

Preliminary analysis of national data from the Health Information
National Trends Survey, conducted by the National Cancer Institute
(NCI), suggests that income and education levels, as well as
gender and age, strongly influence the amount of attention
people pay to health topics (Hesse,
2003). A study by Tu and Hargraves
indicates that level of education is the most important predictor
of health information-seeking; 55 percent of people with postgraduate
education said they sought health information, compared with
only 25 percent of those without a high school diploma (2003).
Education level is also strongly associated with literacy skills,
which are a component of health literacy. The relationship
between education and literacy likely goes both ways: those
who stay in school longer likely have stronger literacy skills,
and those with stronger skills likely stay in school longer.
This relationship indicates that there is much to learn about
how both education and literacy affect people’s access
to, interest in, and engagement with health information and
the pathways for development of communication capacities.

Disabilities

An estimated 54 million Americans—20 percent of the
population—have disabilities (HHS,
2000). Disability, by definition, involves the interaction
of impairments and environmental barriers; removing or reducing
a barrier can reduce a disability. The types of impairments
can include visual, hearing, mobility, cognitive, and learning
disabilities. Each type of impairment corresponds to a set
of accommodations needed to reach a particular audience segment
with effective e-health resources. Disabilities affect people
of all ages, but the proportion of the population affected
increases with age; therefore, because the U.S. population
is aging, the proportion of Americans with disabilities is
growing (HHS, 2005b). There are many crossovers between the
topics discussed in this section and those on the characteristics
and communication needs of older adults and family caregivers,
described below. Although people with disabilities are not
necessarily in poor health, they are at increased risk of secondary
conditions and may have less access to health services and
medical care. Health promotion to improve functioning and reduce
the incidence of secondary conditions has been shown to be
effective (HHS, 2000).

A report by the Pew Internet & American Life Project includes
a “special analysis” on Americans with disabilities
(Lenhart et al., 2003). The research
shows that 38 percent of Americans with disabilities use the
Internet, compared to 58 percent of the entire population.
Users with disabilities are more likely than the general population
to have access only at home (58 percent versus 44 percent,
respectively) as well as more likely to look for medical information
online (75 percent versus 59 percent, respectively). The Pew
research also yielded insights into the reasons persons with
disabilities give for not going online—some of which,
such as misconceptions about the Internet, are amenable to
solution (Lenhart et al., 2003).

For people with disabilities, digital divide issues apply
not only to Internet access but also to a broad set of assistive
and adaptive technologies that increase accessibility of all
kinds. Some of these technologies, which have been likened
to “electronic curb cuts,” enable access to the
Internet and other digital resources for people with disabilities.
Physical barriers to Internet use—or, alternatively,
accommodations—can exist at many points, including the
public access computing site, the computer terminal, the Web
site, the Internet service provider, the browser, and the Web-based
platform. Designing for persons with impairments was rare in
the 40 e-health tools reviewed for this report (see Appendix
1). Only one makes specific accommodations for people with
hearing or visual impairments.

Once physical access to computers and the Internet is achieved,
the next set of issues relates to the design, content, and
delivery of digital information resources. Paradoxically, although
the Internet can reduce the isolation that can come with disability,
it also presents its own barriers that must be overcome before
it can be useful. The specific barrier, and thus the solution,
varies with the impairment, and a detailed review of the often
quite technical ways to achieve accessible Web design is beyond
the scope of this brief overview. The creator of cascading
style sheets, one such mechanism, points out that Web-based
information involves the interaction of “content and
presentation,” and these have to be addressed separately
in order to successfully communicate with people with visual
and hearing disabilities (Bartlett,
2002).

The types of accommodations in content and presentation for
people with disabilities can be beneficial to other e-health
audience segments as well, such as seniors and people with
limited literacy or English proficiency. The accommodations
include multimedia presentation, breaking text into small chunks,
and allowing users to control font size and other visual attributes.
Techniques such as these, together with general principles
of user-centered design and usability testing (described below),
can result in e-health resources that are beneficial to all
people, including those with disabilities.

The problem of inadequate research to guide design and content
decisions figures in this context as it does elsewhere. Apart
from the few references noted above, the present study found
no empirical research on health communication issues for people
with disabilities. This finding was confirmed by staff members
of the National Center on Birth Defects and Developmental Disabilities,
Centers for Disease Control and Prevention, who conducted an
unsuccessful literature search on health communication and
disability in preparation for a health promotion campaign for
women with disabilities (J. Thierry, personal communication,
October 2004).

Developers can draw on a combination of laws, guidelines,
and evaluation tools in achieving and measuring accessibility.
Federal law on accessibility is in Section 508 of the 1973
Rehabilitation Act (revised based on the Americans With Disabilities
Act), which requires that Federal agencies’ electronic
and information technology be accessible to people with disabilities.
An article in the Journal of Medical Internet Research
reported on research that evaluated 108 Web sites for consumer
health information according to disability accessibility guidelines;
the researchers found that Government and educational sites
are the most accessible, presumably at least partly because
of Section 508 requirements for Government sites (www.section508.gov/).
No site met all the criteria, however (Zeng
and Parmento, 2004). Although the requirements only apply
to Federal sites, some private Web developers choose to comply
as well. (See Chiang and Starren,
2004, for another published evaluation of Web access for
people with disabilities).

The World Wide Web Consortium (W3C) Web Accessibility Initiative
has developed its own Web Content Accessibility Guidelines
(WCAG) for determining Web page accessibility (www.w3.org/WAI/).
The Web site of the International Center for Disability Resources
on the Internet leads to a long chain of useful resources (www.icdri.org/prodserv.htm).
The same is true of “Bobby,” a Windows-based tool
that provides a free service to analyze Web pages for their
accessibility to people with disabilities, to identify and
repair barriers to accessibility, and to facilitate compliance
with accessibility guidelines such as Section 508 and W3C’s
WCAG (http://webxact.watchfire.com/).
One expert reports that current Web accessibility guidelines
do not address cognitive disabilities very well, as most of
the focus to date has been on visual and sensory disabilities
(R. Appleyard, personal communication, October 2004, citing
Wehmeyer, 1998, 1999).

Age, Developmental, and Role Issues

As noted above, age is one of the most important factors affecting
health status, information-seeking, media use, and Internet
behaviors. Yet little attention has been paid to life course,
roles (apart from parenting), and experiential variables that
are often associated with age. Each phase of life has its own
developmental perspective, obstacles and facilitating factors,
and unique experiences that influence interests and capacities
related to health communication. For example, unpaid caregiving
by adults for adults is emerging as a critical policy issue
as well as an experiential factor for millions of Americans.
A survey by the National Alliance for Caregiving and AARP estimates
that approximately 44 million adults provide unpaid care to
other adults (National
Alliance for Caregiving and AARP, 2004). The survey finds
that “the typical caregiver is a 46-year-old woman who
has at least some college experience and provides more than
20 hours of care each week to her mother.” Approximately
one-third of caregivers rely on the Internet for information
to help them cope with their caregiving (National
Alliance for Caregiving and AARP, 2004).

Internet use is inversely associated with age. Only 22 percent
of people older than age 65 have been online (Fox,
2004), compared with 96 percent of children and adolescents
age 8 to 18 (Rideout,
Roberts, and Foehr, 2005). The higher percentage of young
people online is to a great extent due to school-based access,
whereas home access remains a concern for the large segment
of low-income children. Home-based access is also important
for older adults, who are more likely to be out of the workforce
or homebound. Partly because of young people’s greater
exposure to technology, training, and technical assistance
opportunities, they show greater comfort and facility with
technology than older adults. (Indeed, some programs involve
them as trainers, as seen in Chapter
5.) Older adults are more likely than persons in other
age groups to have physical or cognitive impairments that further
limit their ability to use computers and navigate the Internet
(Morrell, Dailey, Feldman,
et al., 2003; SPRY
Foundation, n.d.).

However, both groups have shown considerable interest in health
topics. Older adults use their Web access for health purposes
more intensively than other age groups (Fox,
2004); and 68 percent of 15- to 24-year-olds and 50 percent
of all 8- to 18-year-olds who have been online have used the
Internet to get health information (Kaiser
Family Foundation, 2001; Rideout
et al., 2005).

One study is suggestive about the relationships among age,
experience with both health and technology, and use of e-health
tools. It examined participation and nonparticipation rates
by primary care patients with type 2 diabetes in an Internet-based
diabetes self-management support program (Feil,
Glasgow, Boles, et al., 2000). The researchers found no
significant differences in gender, insulin use, computer familiarity,
or computer ownership. The significant differences between
participants and nonparticipants were related to age and years
since diagnosis; younger patients with more recent diagnoses
were more likely to participate.

A relatively recent development of special relevance for older
adults, including the significant percentage who are caregivers,
is the growing use of disease management tools by healthcare
organizations. Older adults have the largest incidence of costly
chronic illnesses, and major institutions such as the Centers
for Medicare & Medicaid Services (CMS) and the U.S. Department
of Veterans Affairs (VA) are investing in the development of
e-health tools to help patients manage their diseases. These
programs provide training and sometimes the necessary equipment.
If this trend continues, at least a small segment of older
adults may be induced to become users of electronic communication
and information for personal health management. In addition,
the Web portal being developed for Medicare beneficiaries introduces
them to an e-health tool that contains content of direct relevance.

Although the specifics vary considerably, both older and young
age groups have style preferences, technology use characteristics,
and health content interests that are often not served by standard
e-health tool content, design, and architecture and that are
best accommodated through targeted tools. The top priorities
for meeting the needs of older and younger users include simplicity
of design and content and the use of multimedia presentations.
One example of applying good design practices and research-based
knowledge of intended users is the Web site for older adults
sponsored by the National Institutes of Health (www.nihseniorhealth.gov).
The site is designed to accommodate limited literacy levels,
cognitive and physical impairments, and different modes of
learning (e.g., textual, visual, auditory). The Web site’s
approach closely matches the general principles of good Web
design for all users promulgated by the Federal Government
(see www.usability.gov
and www.firstgov.gov/webcontent).
Many e-health tools designed for young people have behavior
change and prevention purposes; here the challenge is to make
them interesting and attractive.

Interest in Health Information

Health information-seeking attitudes and behaviors, as well
as attitudes and behaviors toward health care and healthcare
providers, have been identified as a useful basis for segmentation
with respect to e-health communication. Researchers and expert
observers classify people in terms of their degree of independence
and initiative in relation to health care and health information-seeking.
For example, research by the communication firm Porter Novelli
found that the public can be segmented into five health information
types, based on two broad sets of characteristics—degree
of reliance on physicians for health information and level
of activity in seeking out such information (cited in Lieberman
et al., 2004).

The Uninvolved (14 percent) are likely
to describe their health as good or fair; value health
less than others do; expend less energy on prevention;
and exhibit low interest in health information.

Doctor-Dependent Passives (20 percent)
describe their health as excellent or very good; hold lower
values for health and prevention; and express low interest
in health information.

Moderates (28 percent) are generally
healthy adults; value good health and actively try to prevent
disease; and value health information, but do not enjoy
searching for it and may lack skills to do so.

Doctor-Dependent Actives (20 percent)
value health and prevention, but experience more health
problems; and actively seek health information and are
capable of finding it, but may have difficulty interpreting
it.

Independent Actives (19 percent) are
in very good health; highly value health and prevention;
place the highest importance on health information; and
are very skilled at finding and understanding health information.

Long-time online health activist and analyst Dr. Tom Ferguson
proposes a new vocabulary to capture the shift in individuals’
orientation to information and their health. Instead of “consumers”
or “patients,” he sometimes speaks of “medical
end users,” “e-patients,” and “prosumers,”
the last term coined by Alvin Toffler in The Third Wave
to capture the blurring of the distinction between service
providers and recipients (Ferguson,
n.d.). Similar to the Porter Novelli categories, Ferguson
divides patients and consumers into three groups—passive
patients, concerned consumers, and health-active prosumers—and
he predicts an increasing shift into the third group. In addition
to information, he stresses the importance of communication
among consumers, such as in online and face-to-face support
groups.

Dr. Judith Hibbard has developed a multifaceted typology to
assess levels of “health activation” in patients
and consumers (Hibbard,
2003).2 Her work primarily
concerns health behaviors, but it is highly relevant to health
information-seeking and use. Hibbard’s “activation
measure” assesses patients along two axes, one listing
actions the individual can take related to personal health
and the other listing the capacities to be assessed with respect
to those actions (Table 3).

Hibbard states that consumers with higher activation are more
likely to take such actions as read about possible complications
when taking a new medication, seek out health information,
visit a health Web site, and know about treatment guidelines
for their condition. The relevance of her work for the present
report is summarized in two questions she poses:

What kinds of strategies will be most effective in
increasing activation?

How can we take advantage of knowing a patient’s
activation level to tailor an intervention?

Attitudes About Privacy and the Protection of Personal Health
Information

Since the initial framing of this project and drafting of
the report, the issues of protecting personal privacy and ensuring
the confidentiality of personal health information have moved
to the top of the agenda in any discussion of consumer e-health
tools, particularly personal health records. Numerous documents
assert that there must be strong privacy protections for e-health
tools that collect and store personal health information; the
need for strong protections has been particularly noted in
relation to personal health records (Markle
Foundation, 2005; NCVHS,
2005a). Several national surveys have been conducted to
gauge public understanding of privacy issues and the public’s
expectations about privacy protections in an e-health environment
(California
HealthCare Foundation, 2005; Markle
Foundation, 2005; Westin, 2005).
The findings are consistent that a majority expect strong privacy
protections, whether through policies, laws, or technologies.

The findings of two surveys suggest, however, that as in most
other areas, segments of the public can be distinguished on
the basis of their attitudes toward privacy, and likely by
their privacy-protecting behaviors as well (California
HealthCare Foundation, 2005; Westin, 2005). As with other
factors discussed in this chapter, attitudes about health information
privacy and e-health tools have not been well studied. It is
possible to infer from user behavior in online communities,
however, that participants do not perceive all disclosures
of personal information as equal. Participants often post highly
personal and identifiable information in online chats and blogs;
yet a disclosure of the same or similar information as a result
of a security breach of a digital record system would likely
be treated as a privacy violation.

In numerous hearings on personal health records, the National
Committee on Vital and Health Statistics consistently heard
testimony that the key factor for consumers is their ability
to control their own information and records and protect their
privacy (NCVHS, 2005a, 2005b).
In light of the preceding discussion on the diversity in information-seeking
behaviors and activation toward health, the need for control
and sensitivity to disclosures also should be treated as having
a range of values rather than dichotomous values of either
total or no control and sensitivity to disclosure of personal
information.

Designing for Diverse User Groups

Given the number of factors that must be considered when designing
tools to meet the needs of diverse users, it is clear that
a focused effort by developers is required. Engaging persons
with low income or education, different ethnic groups, and
adults with limited literacy skills in health communication
requires sophisticated audience segmentation techniques that
involve intended users of the information in interactive roles
(Freimuth and Mettger,
1990). Targeting (audience segmentation) and tailoring
on communication factors are considered promising strategies
for user-centric design in the electronic environment (IOM,
2002). Both are employed to engage users by personalizing
and individualizing information based on demographic, behavioral,
motivational, psychosocial, or physical characteristics (Brug,
Oenema, and Campbell, 2003).

Targeting or audience segmentation is selecting groups of
users based on common characteristics related to behavior,
health status, or some other common factor. The process of
targeting generally happens in the following sequence. First,
a target audience or market is identified, related to a healthcare
or public health need or a business opportunity. Then, the
audience is analyzed, and if necessary, segmented, to optimize
service and impact. In some cases, specialized products and
services are developed for existing audience segments or new
target audiences. Some tools integrate tailoring capabilities
that make it possible to accommodate individual differences.
This sometimes involves “cultural tailoring,” or
tailoring to enhance the impact for individuals in targeted
audience groups (IOM, 2002).

Tailoring is designed to simulate personal counseling in that
the individual is surveyed and the responses are used to generate
individualized information and feedback (Brug
et al., 2003; IOM
2002).3 Tailored information
has been shown to be more satisfying, read more deeply, seen
as more personally relevant, and more often discussed with
others (Brug et al., 2003).
“First-generation” tailoring involves using a computer
program to generate the individualized feedback that is presented
to the user in a print-based format, such as a letter or newsletter.
“Second-generation” tailoring takes advantage of
the computer’s ability to immediately deliver tailored
information and eliminates the lag time incurred while waiting
for printed, tailored information to be presented (Oenema,
Brug, and Lechner, et al., 2001).

Dr. Victor Strecher and Dr. Kevin Wildenhaus at the University
of Michigan are leading practitioners of computer-based tailoring
in health communication. They prefer tailoring over targeting
to enhance the effectiveness of health communication messages.
When asked to identify the intended user groups or populations
served by the e-tools his lab develops, Dr. Strecher stated,
“Targeted messages miss the important variation in behavioral
predictors that are often found within demographic or even
psychographic groups. Tailoring identifies these predictors
at an individual level and addresses them.” He further
stated, “Our most recent research suggests that deeply
tailored materials seem to help the people who need them the
most—those with low perceived capabilities in solving
problems on their own. Tailoring may particularly help these
individuals by providing a very individualized plan and by
conveying information in a more vivid manner” (V. Strecher,
personal communication, March 16, 2006). The National Cancer
Institute (NCI) has funded Dr. Strecher’s lab to work
on identifying the “active ingredients” that make
computer-based tailoring successful.

Enhancing the usability of Web sites is another strategy to
make e-health tools more fully accessible to all users (Koyani,
Bailey, and Nall, 2003). In the Government context, the
HHS Web team and NCI have played a leading role in developing
and implementing a usability approach to improve the navigation
of Web sites (http://usability.gov).
Usability testing can be used on its own or as part of a broader
approach known as user-centered design.

User-centered design is an iterative process that assesses
tools throughout the design life cycle in terms of users’
preferences and performance. The process includes task and
user analysis and participatory methods, such as focus groups
and surveys, to determine the interests and capacities of prospective
users. Later, usability testing determines how well users are
able to use a given tool, with the goal of uncovering problems
that can be fixed prior to launch. The Think Aloud protocol
is a method in which users describe their thought processes
as they make their way through a Web site. Other methods include
contextual inquiry (observation and testing), interviews, journals,
various forms of inspection, and performance measurement.

The major criteria are users’ success in finding information,
including accuracy and speed; related criteria are likability,
learning, and retention. For example, in one small study, adults
with low literacy were able to learn Web navigation skills
easily and use interactive features such as active graphics
and pull-down menus when the instructions were simple, direct,
and noticeable (Zarcadoolas et al.,
2002).

In an effort to identify the types of user-centric strategies
currently in use by e-health developers in the field, project
staff interviewed 54 developers and other experts about 40
e-health tools designed wholly or partly for diverse users
(see Appendix 1). Each of the tools
proved to be distinctive in the way it combines functions and
features to serve intended users. The analysis of this set
of tools suggests the number of user variables that can be
considered and the many ways developers think about enhancing
relevance and engagement. These developers report that they
often consider literacy levels relevant to the use of e-health
tools, although the literature review in the next chapter indicates
that few studies have systematically included persons with
limited literacy skills, designed tools as health literacy
interventions, or assessed health literacy as part of the evaluation
of the tool.

The scan of 40 e-health tools indicates that developers employ
a variety of strategies to enhance the connection between the
tools and their intended users. The main strategy appears to
be one of targeting or segmentation. The findings align with
the observations made in the IOM
report, Speaking of Health: Assessing Health Communication
Strategies for Diverse Populations, about the adaptation
of health communication for diverse audiences (2002):

Some tools are developed for narrowly defined audiences
(e.g., people over age 65 with chronic obstructive pulmonary
disease; binge-drinking college students). Some developers
have an array of such specialized tools or modules.

Some tools are developed for a broad cross-section
of users, but adapted to serve different audience segments
(e.g., a Spanish-language version, a module for pregnant women,
a chat room for caregivers). The broad cross-section may exist
because the tool is available to all comers (e.g., through
a public Internet site) or because it is distributed to a restricted
but diverse constituency (e.g., employees of a distributor,
health plan enrollees).

Some tools are developed for a broad (and presumably
heterogeneous) user group in a way that focuses on what all
users have in common.

Often, tools are designed for large population segments based
on public health priorities, such as kids with diabetes or
adult smokers who are trying to quit. Several developers mentioned
the economic impracticality of designing highly segmented or
individually tailored tools. Many tools, such as public Web
sites, serve anyone who finds the site on the Internet. Others
may serve anyone in a more restricted but still heterogeneous
group, such as members of a particular health plan or employees
of a large organization. Targeting is often based on one or
two dominant factors, such as shared health issues, gender,
or age. Health condition, risk behavior, and age were the most
popular factors for identifying intended users of e-health
tools. Some developers stated that the most important characteristic
in targeting was the shared health issue, such as people with
cancer and their caregivers, rather than demographic factors.
The implication is that shared health experience is the basis
for coming together via technology. For the majority of the
40 tools, medical conditions (e.g., diabetes) or health-risk
behaviors (e.g., smoking) define the audience.

In all, 19 of the 40 tools in the scan were described as having
one or more special features for one or more diverse groups.
Most consider multiple audience characteristics. The bases
for audience segmentation among the tools (listed in order
of frequency) are age, language, race/ethnicity, gender, income,
geographic location, and disability or sensory impairment.
The segments targeted by these tools include:

Hispanics/Latinos

Other non-English speakers

African Americans

Recent immigrants (e.g., Vietnamese, Caribbean)

Women

Teenagers

Young children

Elders

People with low income

Rural dwellers

Inner-city dwellers

Added to these variations, several e-health tools have versions
for intermediaries or adjunct users such as childcare providers,
teachers, parents, school friends, and public health workers.
The large group of healthcare tools (i.e., tools made available
by healthcare providers or organizations for use by their consumers/patients)
are also used by staff members of the healthcare organization,
such as nurses, administrative staff, and personal physicians,
and these are distinct user groups from the perspective of
tool development and evaluation.

The interviews offer examples of developers who adapted a
single basic program with multiple subprograms based on factors
such as gender, age, or severity of disease. One company has
22 versions of its basic program. This finding suggests that
the often-discussed potential of technology to create customized
versions of generic interventions is starting to be realized
in the marketplace through a variety of approaches.

Summary

This chapter identifies several concepts, factors, and strategies
that can be used to design e-health tools for diverse users.
The concepts of health literacy and meaningful access highlight
the importance of ensuring physical access to information and
technology and designing useful, understandable content. The
IOM has already called for greater attention to communication
factors in the design of health information, messages, and
e-health tools. This chapter elaborates on many of the critical
factors for user-centric design. If the vision of e-health
tools for all is to be realized, these factors, along with
others that have yet to be fully articulated, will require
further research and integration into tool design and development.
A scan of the current field of e-health tools indicates that
developers are beginning to address issues of diversity, but
do not yet have strategies and approaches that go much beyond
traditional public health targeting based on demographic characteristics.
Developers will need to engage consumers more fully in the
research and design process and probe those factors that shape
attitudes, beliefs, values, expectations, and experiences in
relation to health and technology.